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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.07884 |
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| _version_ | 1866908444819193856 |
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| author | Aziani, Alberto Giudice, Michael V. Lo Yazdi, Ali Shadman |
| author_facet | Aziani, Alberto Giudice, Michael V. Lo Yazdi, Ali Shadman |
| contents | Is demand for conspiracy theories online linked to real-world hate crimes? By analyzing online search trends for 36 racially and politically-charged conspiracy theories in Michigan (2015-2019), we employ a one-dimensional convolutional neural network (1D-CNN) to predict hate crime occurrences offline. A subset of theories including the Rothschilds family, Q-Anon, and The Great Replacement improves prediction accuracy, with effects emerging two to three weeks after fluctuations in searches. However, most theories showed no clear connection to offline hate crimes. Aligning with neutralization and differential association theories, our findings provide a partial empirical link between specific racially charged conspiracy theories and real-world violence. Just as well, this study underscores the potential for machine learning to be used in identifying harmful online patterns and advancing social science research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07884 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Conspiracy to Commit: Information Pollution, Artificial Intelligence, and Real-World Hate Crime Aziani, Alberto Giudice, Michael V. Lo Yazdi, Ali Shadman Social and Information Networks Is demand for conspiracy theories online linked to real-world hate crimes? By analyzing online search trends for 36 racially and politically-charged conspiracy theories in Michigan (2015-2019), we employ a one-dimensional convolutional neural network (1D-CNN) to predict hate crime occurrences offline. A subset of theories including the Rothschilds family, Q-Anon, and The Great Replacement improves prediction accuracy, with effects emerging two to three weeks after fluctuations in searches. However, most theories showed no clear connection to offline hate crimes. Aligning with neutralization and differential association theories, our findings provide a partial empirical link between specific racially charged conspiracy theories and real-world violence. Just as well, this study underscores the potential for machine learning to be used in identifying harmful online patterns and advancing social science research. |
| title | Conspiracy to Commit: Information Pollution, Artificial Intelligence, and Real-World Hate Crime |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2507.07884 |